Landslide Displacement Prediction Based on a Deep Learning Model Considering the Attention Mechanism
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摘要: 现有的基于数据驱动的滑坡位移预测模型大多是基于时间序列数据的单点建模，不能考虑整个边坡的变形相关性和滑坡变形的全局建模.为了克服这一缺点，本研究提出了一种基于时空注意（spatial-temporal attention，STA）机制的深度学习模型，该模型将卷积神经网络（convolutional neural network，CNN）与长短时记忆（long short-term memory）神经网络相结合.通过CNN和卷积注意力模块提取滑坡位移的空间变形特征，利用时间注意机制和LSTM模型从外部因素的时间序列数据中捕获重要的历史信息.注意力机制输出的注意权重值可以揭示滑坡变形的时间-空间特征.以三峡库区泡桐湾滑坡为例，对该模型的性能进行了验证.结果表明，STA-CNN-LSTM模型预测的均方根误差（RMSE）和平均绝对百分比误差（MAPE）与传统灰狼算法优化的支持向量机（GWO-SVM）模型相比分别下降了9.28%和13.88%.模型因子权重计算结果表明，在监测期内随着时间的推移，降雨对泡桐湾滑坡变形的影响逐渐增加，而库水位的影响逐渐减小.Abstract: Accurate displacement prediction plays an important role in landslide early warning. However, the majority of the existing data-driven models focus on single-point modeling based on time series data which cannot consider the deformation correlation in the whole slope. To overcome this drawback, this study proposed a spatial-temporal attention (STA) mechanism-based deep learning model which combined the convolutional neural network (CNN) with the long short-term memory (LSTM) neural network. A convolutional block attention module (CBAM) combined with CNN was developed to extract the spatial deformation characteristics of the slope. A temporal attention module and LSTM model were used to learn the significant historical information from the input external conditions time series data. The model also allowed to output the tempo-spatial attention weights to reveal the tempo-spatial characteristics of landslide deformation. The Paotongwan landslide with step-like behavior displacement in the Three Gorges Reservoir Area (TGRA) of China was used to validate the model performance. The results show that, the root mean square error (RMSE) and the mean average percentage error (MAPE) of the STA-CNN-LSTM model decreased 9.28% and 13.88%, respectively, compared with grey wolf optimization optimized support vector machine (GWO-SVM). The attention weight results calculated by STA-CNN-LSTM demonstrate that rainfall had a larger impact on the deformation of the Paotongwan landslide at the beginning of the monitoring while the influence of reservoir water level decreased with ongoing of the monitoring.
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典型元古代环斑花岗岩均产于稳定的地盾, 指示特定的非造山构造环境及超大陆裂解事件, 在芬兰、瑞典、乌克兰等国外以及我国北京密云等地的前寒武地层中相继发现, 近年来受到国内外学者广泛关注和深入研究, 并对环斑花岗岩和环斑结构进行了定义(宋彪, 1992; Rämö and Haapala, 1995; Rämö et al., 1995; 郁建华等, 1996; Rämö and Haapala, 1996; Haapala and Rämö, 1999; Väino and Flodén, 1999).
狭义的环斑结构指的是像芬兰南部维堡环斑花岗岩体中的那种结构, 其特征是卵形的钾长石巨晶具有奥长石-中长石外壳, 也有一些卵球形的钾长石巨晶不具外壳; 钾长石和石英均有2期, 自形成早期的石英是高温的.广义的环斑结构则泛指钾长石巨晶具有斜长石外壳, 钾长石巨晶可以是卵球的, 也可以是自形的, 斜长石外壳可从钠长石(An6) 到中长石(An36).
我国学者卢欣祥等(1999)通过多年研究发现, 在秦岭-昆仑造山带, 沿古缝合带分布着相当数量的环斑或环斑状花岗岩体, 主要为与造山作用有关的印支期环斑花岗岩(卢欣祥等, 1996, 1999).近期, 柴达木北缘鹰峰环斑花岗岩的研究对于环斑花岗岩及秦岭造山带演化等具有重要意义(肖庆辉等, 2003).本文主要对鹰峰环斑花岗岩的环斑结构及地球化学特征进行了初步研究, 并探讨了其形成的构造环境.
1. 岩体地质特征及其时代
鹰峰环斑花岗岩位于柴达木盆地北缘, 柴北缘断裂带的北侧, 也就是柴达木地块与南祁连地体之间的柴北缘构造带, 形成时代为(1 772±33) Ma (肖庆辉等, 2003).鹰峰岩体出露面积约20多km2, 为复式杂岩体, 主要由环斑花岗岩及辉长岩脉组成, 二者构成典型的双峰式岩套.
2. 环斑结构特征
球斑-环斑长石主要为钾长石, 大小1~5 cm不等, 一般为2~3 cm, 含量达60%以上(实际面统计结果).钾长石斑晶边缘有一圈1~1.5 mm的斜长石环边, 形成较典型的环斑结构, 基质由细粒-微粒的石英组成, 受后期构造较明显, 石英有明显的重结晶及定向构造.
钾长石斑晶: 由几个颗粒(成分同为钾长石) 形成聚斑, 中心有一斜长石的内核(图 1a), 斑晶表面具不均匀的高岭土化, 条纹构造明显, 条纹分布且有规律.条纹主要为钠长石, 可见聚片双晶, 干涉色I级黄(薄片稍厚), 格子双晶发育.不同颗粒间有一圈厚度为0.01 mm左右的交代边.钾长石可见包裹有斜长石颗粒和石英颗粒(图 1b, 1c).局部可见钾长石和石英组成的文象交生结构, 钾长石受构造影响, 内部见有较多裂隙, 裂隙中充填有后期(或同期) 进入的钠长石.钠长石沿裂隙穿过2~3个颗粒.
钾长石边缘具斜长石环边(图 1d, 1e), 聚片双晶发育, 绢云母化、高岭土化较强(图 1e).
基质为细粒-微粒结构, 较大斜长石、石英颗粒基质无变形, 较小的石英、角闪石变质强, 具重结晶、定向排列(图 1f), 多平行于斑晶颗粒, 可能为钾长石斑晶在生长过程中对基质造成挤压而形成.
3. 环斑花岗岩地球化学特征
3.1 岩石化学
鹰峰环斑花岗岩以高钾为特征, w (K2O) 达5×10-2以上, w (Na2O) 为3.9×10-2, w (CaO) 为1.8×10-2, w (TiO2) 为0.88×10-2, w (K2O)/w (Na2O) > 1.3, w (K2O)/w (Na2O+K2O) > 0.57, 而w (SiO2) 含量并不高, 平均为67%, w (A)/w (CNK) 平均为0.94, w (A)/w (NK) 约为1.20, w (A)/w (NK)-w (A)/w (CNK) 图解表明属准铝质(图 2).表 1反映出, 本区环斑花岗岩岩石化学特征总体上与A型花岗岩特征相似.
图 2 鹰峰环斑花岗岩w (A)/w (NK)-w (A)/w (CNK) 图解Fig. 2. w (A)/w (NK)-w (A)/w (CNK) diagram for rapakivi granites in Yingfeng表 1 鹰峰环斑花岗岩主元素组成Table Supplementary Table Major element compositions of rapakivi granites in Yingfeng
3.2 微量元素和稀土元素
稀土总量较高, ∑REE达(565~585) ×10-6, Ce含量达210×10-6以上, Zr含量达265×10-6以上, 岩石Ga含量高达25×10-6以上, 10 000*Ga/Al高达3.4以上, 远远高出其他类型花岗岩, 属轻稀土富集型, 但Eu (0.75~4.3) ×10-6轻度亏损(表 2).稀土分布曲线并非呈特征的“海鸥”型, 在图 3 (Pearce, 1983) 上表现为较陡的右倾型, 轻、重稀土分异明显.
表 2 鹰峰环斑花岗岩稀土元素、微量元素组成Table Supplementary Table Rare earth and trace element compositions of rapakivi granites in Yingfeng
图 3 鹰峰环斑花岗岩w (Na2O)-w (K2O) 图解Fig. 3. w (Na2O)-w (K2O) diagram for rapakivi granites in Yingfeng微量元素富集Ba、U、Th、Ce、Hf、Sm, 亏损Sr、Ta、Nb、Zr、Y, 且不含F, 在微量元素蛛网图(图 4b) (Taylor and McLennan, 1985) 上呈明显的Ba、Rb、Th、Ce、Hf、Sm峰和Sr、Ta、Nb、Zr、Y谷, 显示了准铝质A型花岗岩的一般特征.w (Rb)/w (Sr) (0.17~0.6)、w (Rb)/w (Ba) (0.03~0.24) 很低, 反映了岩石的分异演化程度不高.
图 4 鹰峰环斑花岗岩REE及微量元素分布模式Fig. 4. REE and trace elements distribution patterns for rapakivi granites in Yingfeng3.3 鹰峰环斑花岗岩具A型特征
分别对w (Na2O)-w (K2O) (4a) (Collins et al., 1982)、w (Rb)/w (Nb)-w (Y)/w (Nb) (图 5) (Whalen, 1987; Eyb, 1992)和w (La)-w (La)/w (Sm) (图 6) (Trenil, 1978) 进行图解, 结果是: w (Na2O)-w (K2O) 全部落入A型花岗岩区; 鹰峰环斑花岗岩w (La)/w (Sm)-w (La) 呈水平线, 具A型特征(I型花岗岩为倾斜线) 主要以分离结晶作用为主; w (Rb)/w (Nb)-w (Y)/w (Nb) 中样品全部落入A1亚区.上述表明本区花岗岩具A型特征, 且属于A1亚型.
图 5 鹰峰环斑花岗岩w (Rb)/w (Nb)-w (Y)/w (Nb) 图Fig. 5. w (Rb)/w (Nb)-w (Y)/w (Nb) diagram for rapakivi granites in Yingfeng
图 6 鹰峰环斑花岗岩w (La)/w (Sm)-w (La) 图Fig. 6. w (La)/w (Sm)-w (La) diagrams for rapakivi granites in Yingfeng另外Whalen (1987)认为w (Ga)/w (Al) 比值高是A型花岗岩的典型特征, 故鹰峰环斑花岗岩具典型A型花岗岩特征.
4. 构造环境分析
微量元素和稀土元素的地球化学行为, 对于研究花岗岩形成的构造环境具有重要意义. (1) w (K2O)-w (Na2O) (图 3)、w (Rb)/w (Nb)-w (Y)/w (Nb) (图 5)、w (La)/w (Sm)-w (La) (图 6) 图解和高Ga/Al比值表明本区花岗岩具典型A型花岗岩特征, 且属于A1亚型.指示其岩浆来源深, 碱度大, 岩体小, 多与碱性侵入岩、火山岩共生, 环状杂岩体, 大陆或大洋板块内地壳隆起或裂谷拉张环境产物(Trenil, 1978; Collins et al., 1982; Whalen, 1987; Eyb, 1992). (2) 由w (Ce)/w (Gd)-w (Ce)/w (Yb)和w (Gd)/w (Yb)-w (La)/w (Yb) 协变关系(图 7) (Pearce et al., 1984) 表明, 本区的红色环斑花岗岩和灰色环斑花岗岩具有较好的相关性, 说明它们在成因环境上互有联系. (3) w (Nb)-w (Y) (图 8a)和w (Rb)-w (Y+Nb) (图 8b) (Pearce et al., 1984) 图解表明, 本区样品均落入板内花岗岩区或两者的过渡区, 反映构造环境从火山弧向近似板内的方向演化, 即从较活动的挤压环境, 向温度的拉张环境演化.
图 7 鹰峰环斑花岗岩w (Ce)/w (Gd)-w (Ce)/w (Yb) (a), w (Gd)/w (Yb)-w (La)/w (Yb) (b) 协变图Fig. 7. w (Ce)/w (Gd)-w (Ce)/w (Yb) (a), w (Gd)/w (Yb)-w (La)/w (Yb) (b) covariance diagram for rapakivi granites in Yingfeng
图 8 鹰峰环斑花岗岩w (Nb)-w (Y) (a)和w (Rb)-w (Y+Nb) (b)Fig. 8. w (Nb)-w (Y) (a) and w (Rb)-w (Y+Nb) (b) diagram for rapakivi granites in Yingfeng5. 结论与讨论
(1) 鹰峰环斑花岗岩出露于柴达木地块与南祁连地体之间的柴北缘造山带. (2) 鹰峰环斑花岗岩的年龄为(1 772±33) Ma. (3) 结构特征: 钾长石斑晶由几个颗粒(成分同为钾长石) 形成聚斑, 中心有一斜长石内核, 斑晶表面具不均匀的高岭土化, 条纹构造明显, 条纹分布且有规律.基质由细粒-微粒的石英组成, 受后期构造较明显, 石英有明显的重结晶及定向构造. (4) 地球化学特征: 岩石化学组成以高钾为特征, A/CNK < 1, A/NK > 1, 属准铝质; 微量元素组成上富集Ba、U、Th、Ce、Hf、Sm, 亏损Sr、Ta、Nb、Zr、Y, Rb/Sr (0.17~0.6)和Rb/Ba (0.03~0.24) 很低, 岩石分异演化程度不高; 稀土元素: ∑REE、Ce、Zr含量高, Ga含量高达25×10-6以上, 远远高出其他类型花岗岩, 但Eu含量(0.75~4.3) ×10-6轻度亏损, 属轻稀土富集型. (5) 环斑花型花岗岩, 应是一种“干岩浆”, 通常产于挤压造山作用以后的区域拉伸构造环境, 往往是岩石去根作用的产物.通过对其地球化学特征综合分析表明, 鹰峰环斑花岗岩是发生在板内的一种岩浆作用, 是下地壳的麻粒岩受底侵或拆沉作用地幔上涌影响发生部分熔融, 然后经过分异演化形成了碱性的“干”岩浆, 并在后碰撞的拉伸构造环境下侵位(卢欣祥, 2000).同时伴随温度的降低, 钠质的斜长石从钾长石中出溶, 并迁移到钾长石的边沿, 形成了具环斑结构的A1型花岗岩.
上述表明, 鹰峰环斑花岗岩是具环斑结构和A1型花岗岩特征的典型的元古宙环斑花岗岩体, 岩浆组合具双峰式特征.同时, 鹰峰环斑花岗岩产出于柴达木地块与南祁连地体之间的柴北缘造山带, 可以视为中元古代全球超大陆裂解事件的标志(Xiao et al., 2003).
本文在撰写过程中得到张克信、朱云海等老师的热情帮助和支持, 在此一并致谢! -
图 2 彩色数字图像（a）和监测网络空间变形数据（b）在CNN中的数据表示
Fig. 2. Presentation of data in CNNs for (a) color digital images and (b) spatial deformation data in monitoring network
图 7 泡桐湾滑坡的累积位移、库水位及降雨量监测数据
灰色矩形表示滑坡变形的4个明显临时加速度
Fig. 7. Monitoring data of cumulative displacement, reservoir level and rainfall at Paotongwan landslide
图 8 泡桐湾滑坡预测位移与实测位移的比较
Fig. 8. The comparison between the predicted and measured displacement at Paotongwan landslide
图 9 泡桐湾滑坡的空间注意权重分布
Fig. 9. Distribution of spatial attention weights of the Paotongwan landslide
图 10 因子注意权重在泡桐湾滑坡上的分布
Fig. 10. Distribution of factor attention weights at the Paotongwan landslide
图 11 泡桐湾滑坡在多个隐藏时间步长下，STA-CNN-LSTM模型与输入因子的可视化时间注意权重比较
Fig. 11. Visualization temporal attention weights compared with input factors of STA-CNN-LSTM model at multiple hidden time steps in Paotongwan landslide
图 12 4种不同模型预测结果与实际值的比较
Fig. 12. Comparison of monitored and predicted landslide displacements using four prediction models for Paotongwan landslide
表 1 不同模型计算的泡桐湾滑坡位移预测结果的误差
Table 1. Errors of the predicted results for the Paotongwan landslide obtained from different models
监测点 RMSE(mm) MAPE(%) GWO-SVM 1CNN-LSTM CNN-LSTM STA-CNN-LSTM GWO-SVM 1CNN-LSTM CNN-LSTM STA-CNN-LSTM WS01 13.16 11.33 10.76 9.06 4.49 4.32 4.10 2.88 WS02 11.57 11.01 12.17 10.13 3.05 3.25 3.81 2.96 WS03 13.52 12.31 13.47 13.56 5.66 4.96 5.48 5.45 WS04 14.18 14.13 11.21 11.18 4.46 5.82 4.75 4.37 WS05 16.87 16.25 16.12 16.17 9.75 7.53 8.15 6.90 WS06 14.09 18.29 13.89 15.02 6.74 9.94 5.83 6.81 平均值 13.90 13.89 13.10 12.61 5.69 5.97 5.35 4.90 
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